Ship target detection in SAR images based on SimAM attention YOLOv8

Abstract

Deep learning has been widely applied in ship detection in synthetic aperture radar (SAR) imagery due to their powerful feature representation capabilities. However, YOLOv8 models treat all regions of the image equally during convolutional feature processing, resulting in less-than-ideal outcomes. To address this limitation, this study proposes a simple, parameter-free attention module (SimAM) attention-based YOLOv8 algorithm for ship detection in SAR images. The proposed algorithm first passes through a backbone network, which incorporates SimAM attention modules. The SimAM attention mechanism successfully allocates the convolutional neural network's 3D weights effectively using an energy function method, without introducing additional parameters. This mechanism enables the network to automatically emphasize key features in the image, enhancing its ability to represent target areas and suppress background interference. Subsequently, deep features are upsampled and fused with relatively shallow features to extract features at three different scales and achieve target detection, ultimately outputting classification and positional information of the targets. The effectiveness of the model on the SAR-ship-dataset is experimentally validated achieving an mAP50 value of 97.72% and an mAP50-95 value of 68.99%, confirming the superiority of the proposed model.